Automated Detection and Segmentation of Exudates for the Screening of Background Retinopathy

Exudate, an asymptomatic yellow deposit on retina


Introduction
Background diabetic retinopathy (BDR), a systemic disease and long-term diabetes complication, is among the leading sources of blindness worldwide [1]. According to International Diabetes Federation, it is estimated that diabetes will afect 438 million people by 2030 [1]. It is a chronic condition that progresses from mild to moderate and then to irreversible form of diabetic retinopathy [2]. Mild form of diabetic retinopathy is characterized by microaneurysms, whereas exudates characterize moderate form of diabetic retinopathy. BDR has no symptoms and usually does not interfere with vision until the diseases progress to serious form of BDR such as proliferative diabetic retinopathy, which is characterized by neovascularization causing irreversible vision impairment or blindness. Furthermore, if BDR is not diagnosed at the initial stages, it may afect other organs of the body and also therapy becomes less successful at this stage of the disease [2]. It is therefore important to diagnose diabetic retinopathy at an early asymptomatic clinical stage.
In accordance with eye specialists, growth in the number and size of exudates is the primary symptom of DR and is used as an indication to identify the stage of DR [3]. Exudates appear as yellowish deposits in retina with sharp and fne edges. Tey appear in myriad shapes, sizes, and number. Te change in shape, size, and number of exudates in a particular image signifes the development of DR. Exudates are mainly characterized as hard exudates and soft exudates as depicted in Figures 1(a) and 1(b), respectively. Hard exudates, associated with moderate BDR, appear as golden orange glassy fakes with defned edges. Soft exudates, on the other hand, are the manifestation of other forms of retinopathy and are light yellow in colour with blurred contours.
Te detection of seriousness of DR requires qualitative and quantitative study of changes in exudates. Accurate identifcation of hard exudates contour is used for the laser photocoagulation process followed for the treatment of BDR. Eye specialists use manual inspection techniques to identify exudates boundaries in retinal fundus images. Manual marking and subsequently segmentation, as well as the precision with which lesions are assessed and associated criteria, are all hugely dependent on the specialist's skill and expertise. As a result, the uncertainty arises from (i) determining the precise borders due to various shapes and intensities, which may contribute to confusion, and (ii) the probability of missing exudates of a few pixels. Furthermore, manually scanning each retinal fundus image becomes tedious resulting in fatigue. Besides that, many patients are unable to receive efective care due to high costs of tests and scarcity of specialists. Tus, accurate identifcation of hard exudates contours is one of the primary tasks required for the detection of DR and subsequently treating it. Terefore, it is necessary to develop an exudates identifcation system to aid eye specialists, which will be benefcial in lowering the expense of specialists and eliminating the ambiguity related to manual marking. Moreover, exudates identifcation is benefcial not only for diagnosis but also for recovery preparation. Terefore, a computer-aided exudates identifcation and segmentation method of exudates for the diagnosis of BDR has been proposed in this research work. Te rest of the article is organized as follows. Section 2 describes the related work in detail on recent technologies in identifcation of background diabetic retinopathy. Section 3 details the retinal datasets used in this work. Section 4 is about the gradient-based methodology proposed for the segmentation of exudates. Section 4 describes results and discussion, followed by conclusion in Section 5.

Related Work
In order to evaluate the diagnostic capacity of the exudates identifcation and segmentation methods proposed in literature, two types of assessment criteria are used, namely, number of exudates-based assessment and retinal fundus image-based assessments [4].
Number of exudates-based assessment: each spot of exudates in retinal fundus image comprises a number of pixels and single infected image may contain one or more than one spots. Tese spots are segmented by applying exudates identifcation and segmentation method on retinal fundus images. Te assessment of the segmentation results is carried out by comparing results pixel-wise with the reference markings by specialists using mathematically signifcant performance measures. Te assessment results on exudatesbased assessment should depict better performance as precise segmentation is necessary for the treatment planning of BDR.
Retinal fundus image-based assessment: the major goal of retinal image-based assessment is to identify the occurrence or nonoccurrence of exudates to check any incidence of BDR or not. Te results are simply dependent on the occurrence or nonoccurrence of exudates in the fundus. Image-based assessment is mainly carried out by correctly identifying retinal fundus images with exudates and nonexudates. Tis type of assessment is mainly used for clinical purposes, as the precise number of pixels is not signifcant from screening standpoint.
Te image-based assessment method evaluates the method's ability to distinguish between images with the occurrence or nonoccurrence of exudates. However, if the method does not have the capacity to identify contours of exudates, it demonstrates weak performance. Tus, in order to evaluate the method, it is necessary to evaluate the pixel level segmentation results, but it will provide more precise results than image-based assessment. Terefore, both the assessment criteria are key part in judging in the diagnostic capability of the method as well in clinical systems.
A number of computer-aided methods for the identifcation and then segmentation of hard exudates have been developed to aid ophthalmologists for the detection of DR [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Te frst efort using multilevel thresholding was done by Phillips et al. to detect and segment hard exudates [5]. Ege et al. modifed the method designed by Phillips et al. by using combination of various algorithms, namely, thresholding, region growing, and template matching algorithms [6]. Tis method also applied a classifer to categorize hard exudates from other pathological structures present in retinal fundus image. Another approach was suggested by Sanchez et al. in which mixture model using clustering was applied to segment hard exudates [7]. Analogously, fuzzy C-means based method was developed by Yazid et al. to discriminate pixels based on background, blood vessels, and hard exudates pixels [8]. Te major disadvantage of all these methods is that these methods use the criteria that exudates have the highest intensity value as compared to other parts of retinal fundus image and use on intensity-based features to segment exudates. Also, these methods are tested on a limited number of images and thus cannot be generalized to retinal images captured using diferent cameras or datasets. Tus, such methods do not work well for varying type of images having diferent intensity values corresponding to exudates.  [9]. Numerous methods were proposed to detect and then segment exudates by frst eliminating retinal blood vasculature and optic disk from the retinal fundus images [9][10][11] resulting in increase in accuracy. However, these methods were not able to segment landmark structures of retinal images such as optic disk and blood vasculature with diferent shapes and sizes. In order to overcome these disadvantages, Sopharak et al. designed a clustering and morphological processing-based two-step method for the segmentation of hard exudates [12]. Following this development, Welfer et al. applied morphological operators to improve the variations of colours inside retinal fundus images [11]. Tis technique was applied based on the fact that exudates are brighter as compared to the other regions of the retinal fundus image. Subsequently, the authors applied thresholding along with subtraction maxima transformation technique to segregate exudates from background. However, exudates were not precisely detected and resulted in low sensitivity and high spurious responses. In order to improve the sensitivity, Zhang et al. discarded all the anatomical and other structures from retinal fundus images apart from exudates [13]. Exudates pixels were detected using random forest classifer followed by mathematical morphological operators. Te main beneft of mathematical morphology-based approaches is that they have the ability to remove landmark structures such as optic disk and blood vessels from retinal fundus images. However, these methods will not be generalized due to their incapacity of detecting landmark structures of varying shapes and sizes which in turn result in false responses.
Numerous researchers proposed hybrid methods using varying types of supervised approaches to segment hard exudates efectively [14][15][16][17]. Clustering approaches work on the premise of collecting pixels corresponding to exudates.
Majority of these approaches are followed by supervised algorithms to segregate correctly and incorrectly detected exudates pixels during clustering process. Combination of clustering and morphology approach for the detection of exudates was initially applied by Sopharak et al. on a very limited database of forty retinal images [12]. Te method resulted in improved exudates-based sensitivity but very high processing time per image made it inappropriate for practical usage. In order to further improve the medical signifcant parameters for exudates detection, Osareh et al. frst applied enhancement based on intensity values present in retinal images, then clustering algorithm and fnally neural network to precisely segregate exudates from other regions of retinal image [14]. Te major development was then brought by Niemeijer et al. wherein all the bright regions of the retinal fundus images were diferentiated into hard exudates, soft exudates, and drusen [15]. Tey employed probability-based method to frst determine separate bright regions and subsequently classify them into diferent bright lesions structures. Similar method to differentiate various bright lesions using feature extraction using sparse coding was developed by Sidibe et al. However, small bright regions were not detected by the proposed method. Te main limitation of the abovementioned methods is that they are extremely reliant on the type of exudates structures used for training the algorithms.
Lately, deep learning approaches are being applied on retinal fundus images for the identifcation and segmentation of lesions present in the retina. Te deep learning approaches have an advantage of automatic feature extraction on huge number of images. Recently, numerous methods were developed for the detection of exudates and also they achieved promising results [18][19][20][21]. Many researchers used convolutional neural network algorithms for improved accuracy of exudates detection [22][23][24][25]. However, they resulted in low sensitivity indicating a smaller number of correctly detected exudates pixels. Lately, a number of deep learning methods are also being Hard Exudates Journal of Healthcare Engineering developed for disease detection from CT and X-ray images [26,27]. Last year, convolutional neural network-based methods were used for the detection of leaf diseases and COVID-19 detection [28,29]. Te prime concern while using deep learning network is the requirement of large volumes of data and resources for training. Terefore, in this research work, a robust method is designed for the detection of exudates by (i) identifying the true contours of exudates by applying gradient-based approach, (ii) elimination of suspected false exudates pixels by labelling the connected candidate exudates pixels, and (iii) linking the edge pixels for the boundary extraction of exudates. Te main part is that the developed algorithm is evaluated on open source datasets such as STARE and DRIVE [21,22]. High sensitivity/specifcity/accuracy of 95.345/98.63/ 98.04% in the identifcation of exudates boundaries contributes to early detection of patients with DR especially in rural areas.
Te proposed method includes the following three steps: initially the exudates regions are enhanced using enhancement algorithm and also by eliminating artifacts present in the images captured with fundus cameras; in the next step, candidate exudates pixels are detected by applying gradient and linking based method. Lastly, the proposed method was validated by medically signifcant performance measures such as sensitivity, specifcity, and accuracy. To demonstrate the generalisation capacity of the proposed method and applicability for practical usage, the suggested technique is created and evaluated using retinal images obtained from eye hospital as well as four diferent open-source benchmark databases, each with individual features such as capture device, area acquired, angle of acquisition, and resolution. Te exudates segmentation results attained are then utilized by the eye specialists for the recovery planning of BDR. To summarize, the designed approach is intended to assist eye specialists in fnding the severity of the disease and deliver higher accuracy.

Materials
Retinal fundus images database in the present research work is captured from two platforms: 649 retinal images from eye hospital and 658 retinal images from four open-source benchmark databases. Te purpose of using these images from variety of platforms is to test the efciency of the proposed exudates identifcation and segmentation method to be independent of diferent variations in images. Opensource benchmark datasets used are STARE, MESSIDOR, DIARETDB1, and e-Ophtha [30][31][32][33]. Te detailed overview of all the datasets used in this work along with their reference markings is provided below.

Retinal Images Acquired from Eye Hospital. Between
March 2019 and December 2019, 649 retinal images of various intensities, resolutions, contrast, and clarity were obtained from SGHS Hospital, Punjab, India. A total of 375 images comprise hard exudates, 119 with other pathological structures and the remaining with no pathology. Tese images were taken from Topcon fundus camera, which has the resolution of 3218 × 2138 at an angle of 45°. Te reference markings of exudates were provided by the eye specialists. Tese annotations were used as reference to compute the performance of the proposed method. Te clinical supervisor provided the ethical approval to carry out this scientifc study.

Open-Source Benchmark Databases.
Open-source benchmark datasets have varying aims, attributes, and degree of comprehensiveness as discussed below: (a) MESSIDOR dataset: MESSIDOR database comprises of 1200 retinal images acquired using Topcon camera with diferent resolutions at an angle of 45° [ 30]. Four hundred images were shortlisted for the present research work comprising of 274 nonpathological images and 126 comprising hard exudates. It provides only the image-based assessment analysis for the detection of DR. Even though it does not provide manual markings, it is a signifcant source considering the number of accessible images. Tus, to assess the results on these images, manual markings were created by the eye specialists. (b) DIARETDB1 dataset: it contains 89 images taken with a fundus camera at an angle of 50° [31]. Eightyfour images out of 89 exhibit minor symptoms of DR, such as exudates, whereas the other 5 are nonpathological retinal images. Tis dataset was the frst dataset comprising reference markings from eye specialists. Tese markings, on the other hand, solely comprise the enclosures of the pathological regions, and exact markings were generated in the proposed work. (c) STARE dataset: a total of 400 images comprise hard exudates and other pathological structures such as red lesions of various shapes and sizes [32]. Tese images are captured with the Topcon camera which has the resolution of 605 × 700 at an angle of 35°. Image-based DR detection assessment of each retinal image is provided, but does not include reference markings to assess the method based on number of exudates-based assessment. Terefore, in order to assess the exudates-based performance of the proposed method, an eye specialist annotated the contours of the lesions of 96 retinal images having variety of attributes. (d) e-Ophtha EX dataset: it contains 82 images comprising 47 pathological and 35 nonpathological images [33]. Retinal images with varying sizes and shapes of exudates and captured at varying resolutions from OPHIDAT medicine centre are present in this database. Furthermore, e-Ophtha database in the sole database providing exudates-based assessment of lesions, which is useful for comparing the efcacy of various exudates identifcation methods present in literature.

Reference markings of exudates:
open-source benchmark databases do not comprise exact exudates markings or these annotations are no accurate to compute the performance related to exudates-based assessment. As a result, reference markings of all the images from diferent datasets are created under the supervision of eye specialists to precisely analyse the performance of the proposed method.

Methods
Te proposed algorithm in this work, as depicted in Figure 2, is comprised of the following stages, such as (i) enhancement of retinal fundus images, (ii) candidate exudates contour detection, and (iii) precise segmentation of exudates.

Enhancement of Retinal Images.
To enhance the quality of retinal fundus images for the accurate identifcation of exudates, it is necessary to remove artifacts in retinal images. Tese artifacts appear in retinal images due to several aspects such as inappropriate lighting of retinal fundus image, blur, focus, etc. Tese factors highly contribute to degrading the quality of retinal images. Degraded images can further result in inaccurate detection of retinal diseases. Tus, enhancement of images prior to the identifcation of exudates is an essential step. Te algorithm developed for the enhancement of retinal images comprises three steps: (i) multiplicative transformation for elimination of artifacts due to improper lighting, (ii) channel selection for maximum intensity selection, and (iii) Weierstrass transformation for elimination of blur.

Multiplicative Transformation.
Dependency of many factors related to camera, feld of view, and expertise of the clinician results in varying types of artifacts in retinal fundus images. Tese factors result in variation of intensity in varying parts of retinal fundus images. Terefore, in the present work, multiplicative transformation is applied to eliminate the variation of intensity range and enhancing contrast in diferent parts of retina. In the proposed method, the retinal fundus image I (x, y) is considered as a function of lighting l (x, y) and refectance components r (x, y) shown in Lighting component of retinal images represents smooth areas corresponding to low frequency and refectance components represent high frequency components. High frequency components represent the details present in retinal image and thus need to be preserved. Terefore, in order to separate varying frequency components, the Fourier transform (FT) is applied on the image as shown in In the next step, the Fourier transform of the retinal image is fltered using maximally fat magnitude flter M(s, t) given in where the denominator of the flter represents the rate of change of frequency range from pass to cut-of range. Further, the transformed image is represented as In the last step, the resultant image is then converted from the frequency domain by applying inverse Fourier transform resulting in preprocessed image. Te image is fnally termed as I P (x, y).

Channel for Maximum Intensity.
Te preprocessed retinal image contains RGB components. Among them, the green component represents the maximum intensity with respect to other structures present in retinal images and is thus selected for further processing.

Weierstrass Transformation. Te blur present in ret-
inal fundus images has a distribution similar to Weierstrass transformation [34]. Hence, it is removed by using the smoothing Weierstrass flter. Te fnal enhanced image after the application of Weierstrass flter is thus represented as I PE (x, y).

Candidate Exudates Contour Detection.
Exudates contour identifcation is the initial and signifcant tasks in the precise tracing of exudates edges. In order to identify the exudates contour, the following steps are designed in the proposed work.

Gradient Calculation.
Te enhanced image obtained in the previous step is frst passed through the Sobel edge detection flter to calculate the intensity variation using Del operator in both directions of Cartesian coordinates I PEH and I PEV , respectively. Te edge images obtained for both directions are used to calculate amplitude I PEamp (x, y) and angle I PEang (x, y).
Journal of Healthcare Engineering

Preservation of Maximum Intensity Pixels in
Neighbourhood. Te edge images obtained in both directions are examined to eliminate unnecessary pixels not corresponding to exudates edges. Te elimination process is carried out by analysing the entire image to determine whether it belongs to maximum intensity in the neighbourhood or not. It is done by comparing the pixel under consideration with all the other pixels of the neighbourhood. If the intensity value of the pixel under consideration is highest, then the pixel is kept otherwise eliminated.

Elimination of Weak Exudates Pixels by Contour
Tracing. Te resultant image of the last stage contains candidate exudates pixels. However, the application of the last step may result in false exudates pixels due to variation of intensities and artifacts in retinal images. In order to segregate the false exudate pixels, a dynamic thresholding method is designed in this work. Te amplitude image I PEamp (x, y) is threshold by choosing two set of threshold values, namely, higher and lower threshold values, respectively. If the intensity value of I PEamp (x, y) is more than the higher threshold value, pixel is preserved and termed as confrmed exudates pixels. On the other hand, if the intensity value of I PEamp (x, y) is less than the lower threshold value, pixel is termed as false exudate pixel and eliminated. Also, if the pixel intensity value is more than the lower threshold value and less than the higher threshold value, the pixel is termed as weak exudates pixel. Te decision on such pixels is taken based on the fact of whether these pixels are connected to confrmed exudates pixels or not. If they are connected, then these will be preserved along with confrmed exudates pixels; otherwise they will be eliminated.  not connected due the nature of flters used. However, in order to extract the precise boundary of exudates, it is signifcant to link the exudates pixels extracted in the last step. Terefore, the steps followed to extract the exudates are the following.

Exudates Edge Diminishing.
Te candidate exudates pixels forming the exudates edges determined in the last step appear to be broken parts of exudates contours. Te true edge contours are connected pixels forming clusters. However, the pixels which are not in the vicinity of the clusters are not the true exudates pixels and are to be eliminated. Tese insignifcant false pixels are eliminated with the help of morphology diminishing operators.

Retrieval of Exudates Corner Points.
Te exudates edges in retinal images obtained in the previous step occasionally form the closed connected structures. Te precise contour of exudates can be obtained only by connecting the corner points of exudates accurately. Terefore, the corner points estimation is necessary to connect the exudates edges. Tese corner points are retrieved by traversing the 4 × 4 array in the eight directions, as shown below. Te corner point pixel is assumed to be in the centre of this array. Te array values denoted by "s" determine whether the centre pixel is the corner point or not. If at least one of these values is high, i.e., 1, the centre pixel is considered to be the candidate edge pixel; otherwise it is eliminated.
s s s

Marking of Exudates Corner Points and Contour
Detection. Te corner points of exudates retrieved in the previous step are marked to connect the exudates pixels accurately. A same mark is allocated to the set of exudates pixels depicting similar characteristics. Te breaks in the exudates structures are linked by assessing the spatial properties of the corner points. Lastly, the contour of exudates is detected by connecting the segments of exudates identifed. Exudates segments are connected by neighbourhood analysis method. Te designed method comprises 3 steps: (1) Examine the features of exudate pixels in the neighbourhood of each marked exudate pixel. (2) Pixels having the similar features are connected, resulting in forming the contour of exudates. (3) Exudates pixels are connected if the diference between magnitude and angle between pixels under consideration is less than the predetermined magnitude and angle threshold, respectively.

Results and Discussion
Te proposed exudates identifcation and segmentation method is designed and realized in MATLAB version 2018a on a PC with Intel Core i7 processor. Retinal images from hospital and four open-source benchmark databases, as mentioned earlier, are utilized to enumerate the performance of the proposed computer-aided method. A series of experiments were conducted to adjust the threshold values range at diferent steps. Te optimal intensity thresholds are the ones that achieve best sensitivity/specifcity combination. Increase in sensitivity indicates the correct detection of bright as well as subtle exudates, whereas higher specifcity shows that the method does not recognize a nonexudates pixel as an exudates pixel. A total of 1307 retinal images comprising 525 nonpathological, 623 with exudates, and 157 with other retinopathies with diferent characteristics such as colour, location, resolution, etc. are used in this work. Te performance validation of the designed method is done by (i) visual evaluation of expert ophthalmologist and (ii) medically important statistical measures such as sensitivity, specifcity, accuracy, and positive predictive value [35]. Tese statistical measures are enumerated at two diferent levels, i.e., image-based assessment and exudates-based assessment. Image-based assessment diferentiates images based on the existence or nonexistence of exudates and thus aids in mass screening of patients with background diabetic retinopathy. However, exudates-based evaluation discriminates exudates pixels from nonexudates pixels. Tis evaluation is primarily used in treatment process of diabetic retinopathy wherein precise boundary exudate markings are required for laser photocoagulation. shows that the designed method segments minute exudates which were not even marked by experts. Figure 3(c) shows the exudates with the fuzzy boundaries. Te segmentation results by the proposed method in Figure 3(f ) depict that few exudates pixels are missed, whereas few are wrongly detected. Tis is due to the fact that, in these images, exudates pixels mix with the background resulting in fuzzy boundaries. Also, the ophthalmologists opined that out of 623 retinal fundus images 612 images are segmented in such a way that it can be used for screening as well as treatment process. Out of the remaining 11 retinal fundus images, 9 were with fuzzy exudates boundaries and can only be used for mass screening and the segmentation results of 2 images were not suitable and hence rejected.  [35]. Additionally, the overall segmentation results of the designed computeraided method indicate clearly the substantial performance of the proposed exudates segmentation method. Te bar graphs in Figure 4 depict the mean sensitivity, specifcity, and accuracy along with the deviations around the mean value. Tese bar graphs depict the overall high value of particularly sensitivity of 93.44% and the mean deviation of 2.63% as compared to the method of Kaur and Mittal which showed the sensitivity of 88.85% and deviation of 4.73%. On comparing other parameters in Figure 4, it can be examined that the designed method has the substantial contribution in attaining the overall mean specifcity and accuracy of 97.22% and 95.68%, respectively. Also, the mean deviation values are also comparable to the method proposed by Kaur and Mittal [35]. From Table 1, the proposed computer-aided method clearly outperformed all the exudates-based segmentation methods, thus rationalizing the segmentation results.   [41] is that the method has been validated on only 45 retinal fundus images. Table 1 depicts that the overall sensitivity, specifcity, and accuracy ----------------Jaya et al. [37] Clinical 200 94.1% 90% 93% ------------Welfer et al. [11] DIARETDB1 89 70.4% 98.84% ----------------Niemeijer et al. [15] Clinical 300 ------------95% 86% ----Youssef et al. [38] Clinical 7 ------------80% 100% ----Lahmiri et al. [39] STARE 45 ------------100% 100% 100% Osareh et al. [14] Clinical 142 93% 94.1% ----95% 88.9% ----Gracia et al. [40] Clinical 117 88.08% --------100% 83.95% 93.53% Sanchez et al. [7] Clinical 80 90.2% --------100% 90% ----Harangi et al. [41] DIARETDB1 89 63% 85% ----89% --------Kaur and Mittal [35] Multiple  on the retinal images acquired from hospital are 97.45%, 99.76%, and 98.65%, respectively, for the image-based assessment criteria. Tese results clearly indicate the capability of the proposed method to discriminate retinal images based on the absence or presence of exudates. Moreover, it can also be observed that the proposed method shows the best performance for sensitivity, specifcity, and accuracy for clinical images acquired. Conclusively, the proposed method has achieved high values of medically signifcant statistical measures in comparison to the other methods. Terefore, subjectively as well as objectively, the designed exudates segmentation method is robust in contrast to the exudates segmentation methods proposed in literature.

Conclusion
A new exudates segmentation computer-aided method for the detection of background diabetic retinopathy has been designed in the present research work. Te designed computer-aided method efciently segments exudates in images of retina as it involves the steps such as (i) enhancement of retinal fundus images, (ii) candidate exudates contour detection, and (iii) precise segmentation of exudates. Te performance of the designed exudates segmentation computer-aided method has been validated on 1307 images of retina from two sources, namely, eye hospital and four open-source benchmark databases. Results of the visual evaluation of the expert ophthalmologist are supported by the results obtained by the medically signifcant performance parameters, namely, exudatesbased evaluation and image-based evaluation. Te exudates-based evaluation outcomes specify the high precision of the designed method in correct detection of exudates pixels with sensitivity of 93.44%. Te exudates-based evaluation results thus reveal the capability of the designed method in treatment process of background diabetic retinopathy. Te specifcity of 97.22% shows the ability of the method in successfully diferentiating the exudates pixels from the nonexudates pixels. In addition, the proposed method outperformed another recent exudates segmentation method in terms of image-based evaluation with the accuracy of 98.04%. Terefore, the proposed method shows its potential in mass screening of patients with background diabetic retinopathy. Lastly, it can be highlighted the proposed computer-aided method may be used to assist the expert ophthalmologists in the preliminary detection and diagnosis of background diabetic retinopathy.